Photograph aesthetical evaluation and classification with deep convolutional neural networks

Tan, Yunlan; Tang, Pengjie; Zhou, Yimin; Luo, Wei; Yong-ping, Kang; Li, Guangyao

doi:10.1016/j.neucom.2016.08.098

Cited by 35 publications

(14 citation statements)

References 38 publications

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“…Computer vision methods such as ‘sparse coding’ [ 22 ] and ‘bag of visual words’ [ 23 ] have allowed researchers to identify statistical characteristics and specific areas of images that relate to concepts such as ‘artistic style’ [ 24 ] or visual perceptions of cities [ 25 ]. More recently, the introduction of convolutional neural networks (CNNs) has led to dramatic improvements in computer vision tasks, including visual recognition [ 26 , 27 ], understanding image aesthetics [ 28 , 29 ] and extracting perceptions of urban neighbourhoods [ 30 , 31 ].…”

Section: Introductionmentioning

confidence: 99%

Using deep learning to quantify the beauty of outdoor places

2017

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Beautiful outdoor locations are protected by governments and have recently been shown to be associated with better health. But what makes an outdoor space beautiful? Does a beautiful outdoor location differ from an outdoor location that is simply natural? Here, we explore whether ratings of over 200 000 images of Great Britain from the online game Scenic-Or-Not, combined with hundreds of image features extracted using the Places Convolutional Neural Network, might help us understand what beautiful outdoor spaces are composed of. We discover that, as well as natural features such as ‘Coast’, ‘Mountain’ and ‘Canal Natural’, man-made structures such as ‘Tower’, ‘Castle’ and ‘Viaduct’ lead to places being considered more scenic. Importantly, while scenes containing ‘Trees’ tend to rate highly, places containing more bland natural green features such as ‘Grass’ and ‘Athletic Fields’ are considered less scenic. We also find that a neural network can be trained to automatically identify scenic places, and that this network highlights both natural and built locations. Our findings demonstrate how online data combined with neural networks can provide a deeper understanding of what environments we might find beautiful and offer quantitative insights for policymakers charged with design and protection of our built and natural environments.

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Section: Introductionmentioning

confidence: 99%

Using deep learning to quantify the beauty of outdoor places

2017

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“…A basic stage in a CNN is composed of a convolutional layer and a pooling layer [ 36 ]. Each level consists of a certain number of feature maps, which means that CNNs have a good hierarchical feature representation ability from a lower level to a higher level [ 37 ]. Through the propagation of a CNN, the feature map’s size will decrease layer by layer and the extracted features are more global.…”

Section: Learning Methodsmentioning

confidence: 99%

An Intelligent Gear Fault Diagnosis Methodology Using a Complex Wavelet Enhanced Convolutional Neural Network

et al. 2017

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As a typical example of large and complex mechanical systems, rotating machinery is prone to diversified sorts of mechanical faults. Among these faults, one of the prominent causes of malfunction is generated in gear transmission chains. Although they can be collected via vibration signals, the fault signatures are always submerged in overwhelming interfering contents. Therefore, identifying the critical fault’s characteristic signal is far from an easy task. In order to improve the recognition accuracy of a fault’s characteristic signal, a novel intelligent fault diagnosis method is presented. In this method, a dual-tree complex wavelet transform (DTCWT) is employed to acquire the multiscale signal’s features. In addition, a convolutional neural network (CNN) approach is utilized to automatically recognise a fault feature from the multiscale signal features. The experiment results of the recognition for gear faults show the feasibility and effectiveness of the proposed method, especially in the gear’s weak fault features.

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“…Extracting features using CNN is performed automatically by the computer, and the high-level features of one entire image can be obtained without manual involvement and extensive expert knowledge. Tan et al [19] introduced an aesthetic photo classifier with a deep and wide CNN, which can be applied to fine-grained aesthetic quality prediction. Kairanbay et al [20] not only used deep CNN to extract aesthetic features but also used the global average pool to reduce the complexity of CNN.…”

Section: Related Workmentioning

confidence: 99%

A Novel Feature Fusion Method for Computing Image Aesthetic Quality

Zhang

et al. 2020

IEEE Access

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Computationally, the aesthetic quality of an image means that the model automatically scores the aesthetic level of the image. However, there are many factors that determine beauty or ugliness for photographic photos. Therefore, extracting a variety of representative aesthetic features and fusing these features are still difficult tasks. In this paper, we design a two-stream network to calculate the aesthetic quality of the image. The upper stream of the network is an improved network with the SEResNet-50 and six skip connections added, which can improve the performance of the model without training to obtain deep convolutional neural network features. The lower stream of the network consists of the proposed algorithms for handcrafted extracting aesthetic features and multiple convolution layers to extract the aesthetic features. Finally, to fuse the features of the two-stream network without adding feature dimensions, a novel feature fusion layer is proposed. The results show that this novel feature fusion method can calculate results close to the artificial aesthetic evaluation. INDEX TERMS Deep convolutional neural networks, feature fusion, handcrafted aesthetic features, image aesthetics quality assessment.

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Photograph aesthetical evaluation and classification with deep convolutional neural networks

Cited by 35 publications

References 38 publications

Using deep learning to quantify the beauty of outdoor places

Using deep learning to quantify the beauty of outdoor places

An Intelligent Gear Fault Diagnosis Methodology Using a Complex Wavelet Enhanced Convolutional Neural Network

A Novel Feature Fusion Method for Computing Image Aesthetic Quality

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